We use the k-nearest neighbor technique to probe the instance representation. We first generate a small gallery set by random sampling of image crops that vary spatially and in scale from a single image. We then use another query image crop from the same semantic category to rank the gallery set. We resize these image crops to \(224\times 224\)  and extract the instance representations for measuring similarities. In Figure REF , the top nearest retrieval returns the image bounding box with the closest spatial and scale configuration as the query crop. The results suggest that the instance representation is sensitive to spatial and scale changes, and the learned high-level representation is not a result of invariance but is more powerful and generalizable. This example also demonstrates a form of zero-shot detection using exemplars [1]}.
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